runBenchmarks {DHARMa}

R Documentation

Benchmark calculations

Description

This function runs statistical benchmarks, including Power / Type I error simulations for an arbitrary test with a control parameter

Usage

runBenchmarks(calculateStatistics, controlValues = NULL, nRep = 10,
  alpha = 0.05, parallel = FALSE, ...)

Arguments

calculateStatistics	the statistics to be benchmarked. Should return one value, or a vector of values. If controlValues are given, must accept a parameter control
controlValues	optionally, a vector with a control parameter (e.g. to vary the strength of a problem the test should be specific to). See help for an example
nRep	number of replicates per level of the controlValues
alpha	significance level
parallel	whether to use parallel computations. Possible values are F, T (sets the cores automatically to number of available cores -1), or an integer number for the number of cores that should be used for the cluster
...	additional parameters to calculateStatistics

Value

A object with list structure of class DHARMaBenchmark. Contains an entry simulations with a matrix of simulations, and an entry summaries with an list of summaries (significant (T/F), mean, p-value for KS-test uniformity). Can be plotted with plot.DHARMaBenchmark

Note

The benchmark function in DHARMa are intended for development purposes, and for users that want to test / confirm the properties of functions in DHARMa. If you are running an applied data analysis, they are probably of little use.

Author(s)

Florian Hartig

See Also

plot.DHARMaBenchmark

Examples

# define a function that will run a simulation and return a number of statistics, typically p-values
returnStatistics <- function(control = 0){
  testData = createData(sampleSize = 20, family = poisson(), overdispersion = control, 
                        randomEffectVariance = 0)
  fittedModel <- glm(observedResponse ~ Environment1, data = testData, family = poisson())
  res <- simulateResiduals(fittedModel = fittedModel, n = 250)
  out <- c(testUniformity(res, plot = FALSE)$p.value, testDispersion(res, plot = FALSE)$p.value)
  return(out)
}

# testing a single return
returnStatistics()

# running benchmark for a fixed simulation, increase nRep for sensible results
out = runBenchmarks(returnStatistics, nRep = 5)

# plotting results depend on whether a vector or a single value is provided for control
plot(out) 

# running benchmark with varying control values
# out = runBenchmarks(returnStatistics, controlValues = c(0,0.5,1), nRep = 100)
# plot(out)

# running benchmark can be done using parallel cores
# out = runBenchmarks(returnStatistics, nRep = 100, parallel = TRUE)
# out = runBenchmarks(returnStatistics, controlValues = c(0,0.5,1), nRep = 10, parallel = TRUE)

# Alternative plot function using vioplot, provides nicer pictures 

# plot.DHARMaBenchmark <- function(x, ...){
#   
#   if(length(x$controlValues)== 1){
#     vioplot::vioplot(x$simulations[,x$nSummaries:1], las = 2, horizontal = T, side = "right", 
#                      areaEqual = F,
#                      main = "p distribution under H0",
#                      ylim = c(-0.15,1), ...)
#     abline(v = 1, lty = 2)
#     abline(v = c(0.05, 0), lty = 2, col = "red")
#     text(-0.1, x$nSummaries:1, labels = x$summaries$propSignificant[-1])
#     
#   }else{
#     res = x$summaries$propSignificant
#     matplot(res$controlValues, res[,-1], type = "l", 
#             main = "Power analysis", ylab = "Power", ...)
#     legend("bottomright", colnames(res[,-1]), 
#             col = 1:x$nSummaries, lty = 1:x$nSummaries, lwd = 2)    
#     
#   }
# }

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